This repository contains the code to setup the final evaluation of the course "Machine Learning: Project" (KU Leuven, Faculty of Engineering, Department of Computer Science, DTAI Section).
The departmental computers will be used to run a tournament and submit your implementation (see detailed instructions below). You can also use these computers to train your agents. A tutorial to connect remotely via SSH can be found here and additional info is available on the departmental web pages.
You will see a personal directory in:
/cw/lvs/NoCsBack/vakken/ac2122/H0T25A/ml-project
There is an upper limit of 50MB on the disk space that you can use. Remote (ssh) users are also limited to 2GB of RAM.
OpenSpiel and other packages that you can use are pre-installed in a virtual environment, which can be activated using:
source /cw/lvs/NoCsBack/vakken/ac2122/H0T25A/ml-project/venv/bin/activate
Since this virtual environment will be used to run the tournament, you should avoid language features that are not compatible with the installed Python version (3.8.10) or use packages that are not installed. All of OpenSpiel's required and optional dependencies are currently installed.
This section describes how get started with using FCPA in OpenSpiel.
First, install OpenSpiel as described on the github site. To guarantee compatibility with the installation on the departmental computers, you should use v1.0.2 of OpenSpiel. You can download this version with:
git clone -b 'v1.0.2' --single-branch --depth 1 https://github.com/deepmind/open_spiel.git
Importantly, you must install from source and prepend a flag that will ensure it compiles the optional dependency on the ACPC poker engine.
cd /path/to/open_spiel
OPEN_SPIEL_BUILD_WITH_ACPC=ON ./install.sh
OPEN_SPIEL_BUILD_WITH_ACPC=ON ./open_spiel/scripts/build_and_run_tests.sh
Next, update your PYTHONPATH as discussed in OpenSpiel's installation instructions, reload the shell if necessary, and activate the virtual environment.
To make sure everything works, you can try to execute the example script:
python3 python/examples/poker_fcpa_example.py
This will run two random players in FCPA poker. You can also play fixed policies like always-call and always-fold, in addition to playing against them yourself on the keyboard by passing flags:
python3 python/examples/poker_fcpa_example.py \
--player0=random --player1=human
The tournament will be played with agents that are available on the departmental computers. This will allow you to try your agent in the identical environment that is used by the tournament script. For this to work, you have to adhere to the following setup:
- Your agent extends the
Agentclass provided in the filefcpa_agent/fcpa_agent.py. - The tournament code will scrape the directory provided for you on the departmental computers for the
fcpa_agent.pyfile and call theget_agent_for_tournamentmethod. If multiple matching files are found, a random one will be used. - Your agent should be ready to play in a few seconds, thus use a pre-trained policy. An agent that is not responding after 10 seconds will forfeit the game.
Make sure you do not use relative paths in your implementation to load your trained model, as this will fail when running your agent from a different directory. Best practice is to retrieve the absolute path to the module directory:
package_directory = os.path.dirname(os.path.abspath(__file__))Afterwards, you can load your resources based on this package_directory:
model_file = os.path.join(package_directory, 'models', 'mymodel.pckl')If you use Tensorflow you cannot use the default graph. This will give problems when playing against other agents in the tournament. You can use the following code to create a new graph:
self.graph = tf.Graph()
sess = tf.Session(graph=self.graph)
sess.__enter__()
with self.graph.as_default():
pass # code needing sess goes hereIf you prefer to program in C++, you can also use OpenSpiel's C++ API. Although, you will still have to write a Python wrapper to be able to participate in the tournament. To compile C++ code on the departmental computers you can use the g++ compiler.
To submit your agent, a copy of you code and agent needs to be available on the departmental computers in a directory assigned to you (only your own code, openspiel and other libraries are provided). Also the code to train your agent should be included.
The departmental computers have openspiel and its dependencies installed such that you can verify that your agent works. During the semester the tournament script will be run to play games between the (preliminary) agents that are already available. A tentative ranking will be shared.
Tensorflow is not compatible with Python3.10. Use Python3.9 or earlier.
On macOS you can use an older version by running these commands before the install script:
brew install python@3.9 # if using homebrew
virtualenv -p /usr/local/opt/python@3.9/bin/python3 venv
. ./venv/bin/activate
Install the required packages (in the virtual environment).
pip install -r requirements.txt
First, check if the pyspiel module is available in build/python. If it's absent compilation failed. Try compiling again.
Second, make sure the modules can be found by Python by setting the PYTHONPATH environment variable:
export PYTHONPATH=.:./build/python:$PYTHONPATH
If you encounter this error on the departmental computers, make sure to activate the virtual environment (see above).
Most compilers will allow an empty return statement, but some do not.
open_spiel/open_spiel/higc/referee_test.cc:229:47: error: return-statement with no value, in function returning ‘int’ [-fpermissive]
229 | if (absl::GetFlag(FLAGS_run_only_blocking)) return;
You can easily fix this by replacing return; with return 0; in the source code.